Commit bb3cc9de authored by Oleg Dzhimiev's avatar Oleg Dzhimiev

1. adding color from np.array

parent befe9e85
...@@ -588,8 +588,6 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope ...@@ -588,8 +588,6 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
tmp1.append(ts) tmp1.append(ts)
imsum2 = tf.concat(tmp1,axis=0) imsum2 = tf.concat(tmp1,axis=0)
print("imsum2 shape: ")
print(imsum2.shape)
tf.summary.image("inter_w8s",tf.reshape(imsum2,[1,layout[index]*cluster_side*(block_side+1)//4,4*cluster_side*(block_side+1),3])) tf.summary.image("inter_w8s",tf.reshape(imsum2,[1,layout[index]*cluster_side*(block_side+1)//4,4*cluster_side*(block_side+1),3]))
...@@ -857,6 +855,9 @@ with tf.Session() as sess: ...@@ -857,6 +855,9 @@ with tf.Session() as sess:
merged = tf.summary.merge_all() merged = tf.summary.merge_all()
vis_placeholder = tf.placeholder(tf.float32, [1,32,325,3])
some_image2 = tf.summary.image('custom_test', vis_placeholder)
l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1))) l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2))) l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE): with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
...@@ -968,7 +969,24 @@ with tf.Session() as sess: ...@@ -968,7 +969,24 @@ with tf.Session() as sess:
# _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg}) # _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg})
train_writer.add_summary(some_image.eval(), epoch) #train_writer.add_summary(some_image.eval(), epoch)
l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
w = tf.get_variable('weights',shape=[325,32])
wd = w[tf.newaxis,...]
wds = tf.stack([wd]*3,axis=-1)
timg_min = tf.reduce_min(w).eval()
timg_max = tf.reduce_max(w).eval()
timg = wds.eval()
timg[:,:,:,0] = timg_min
timg[:,:,:,1] = timg_min
timg = np.transpose(timg,(0,2,1,3))
train_writer.add_summary(some_image2.eval(feed_dict={vis_placeholder: timg}), epoch)
train_writer.add_summary(train_summary, epoch) train_writer.add_summary(train_summary, epoch)
test_writer.add_summary(test_summaries[0], epoch) test_writer.add_summary(test_summaries[0], epoch)
......
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